Machine Learning
Acute myocardial infarction (AMI) is the main cause of death in developed and developing countries. AMI is a serious medical problem that necessitates hospitalization and sometimes results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Many studies have been conducted on the prognosis of AMI with hemogram parameters. However, no study has investigated potential hemogram parameters for the diagnosis of AMI using an interpretable artificial intelligence-based clinical approach.
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The dataset consists of measurements of four different stages of degradation in low-voltage contactors used for industrial purposes. The measurements were obtained with fiber Bragg grating (FBG) sensors that detect the dynamic deformation generated in switching under different internal components. The measurements were processed and features from PSD, FFT and TSFEL python library were extracted. The features of PSD and FFT were acquired in 40 sliding windows of 50Hz from the signal.
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This data set contains:
- Training dataset: 271 CT-scans of inner ears used for optimization and training of the model.
- Validation dataset: 70 CT-scans of inner ears used for external validation.
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323 Views
Dataset1
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Nowadays, how to non-destructively obtain the bending strength of the bronze-based matrix fabricated by low-temperature hot pressing sintering (HPS) is still difficult. The main contribution of our research is a proposed visual quantization model based on microstructure features of metallographic microscopy images and machine learning to predict the bending strength of bronze-based material. Exploring the interrelationship between microstructure features and mechanical properties will guide the modulation of grinding wheel composition and HPS parameters.
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Data on 2355 COVID-19 cases by date of July to December 2021 were extracted from a data set recorded by COVID-19 referral centers at Qazvin province in Iran. We recorded a wide range of clinical characteristics including age, sex, previous diseases, and hospitalization time. Moreover, we collected data about the different consumed medications including Atrovastatin 20 mg, Atrovastatin 40 mg, Ivermectin 3 mg, Ivermectin 40 mg, Dexamethasone, Kaletra, Favipiravir, Famotidine 40 mg, Interferon, Remdesivir, Hydroxychloroquine.
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389 Views
During our research in generating or optimizing molecules to be drug candidates by extending deep reinforcement learning and graph neural networks algorithms, we used GEOM data [1], and we had an idea to make a dataset obtained from molecules from GEOM to predit the activity towards COVID and the drug linkeness. We calculated over 200 descriptors for the molecules using RDKit [2]. We hope you enjoy using it.
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This dataset comprises gunshot audio and supporting data released as part of ShotSpotter Tech Note 098, "Precision and accuracy of acoustic gunshot location in an urban environment".
The data derive from a series of live fire tests of the ShotSpotter Respond gunshot location system conducted in Pittsburgh, PA on December 18th, 2018 by the Pittsburgh Bureau of Police. ShotSpotter uses live fire tests to validate that the deployed sensor density is appropriate for the community in question, and to ensure the system is ready for production use.
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The pathology files of 194 colon cancer patients, 137 breast cancer patients, 124 gastric cancer patients, and 169 thyroid cancer patients who were referred to the healthcare facilities of Qazvin Province, Iran were examined for age, sex, surgery type, and pathological information. We collected information between 2010 and 2020.
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1011 Views